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Image denoising algorithms often assume an additive white Gaussian noise (AWGN) process that is independent of the actual RGB values. Such approaches are not fully automatic and cannot effectively remove color noise produced by today’s CCD digital camera. In this paper, we propose a unified framework for two tasks: automatic estimation and removal of color noise from a single image using piecewise smooth image models. We introduce the noise level function (NLF), which is a continuous function describing the noise level as a function of image brightness. We then estimate an upper bound of the real noise level function by fitting a lower envelope to the standard deviations of per-segment image variances. For denoising, the chrominance of color noise is significantly removed by projecting pixel values onto a line fit to the RGB values in each segment. Then, a Gaussian conditional random field (GCRF) is constructed to obtain the underlying clean image from the noisy input. Extensive experiments are conducted to test the proposed algorithm, which is shown to outperform state-of-the-art denoising algorithms.
image denoising, piecewise smooth image model, segmentation-based computer vision algorithms, noise estimation, Gaussian conditional random field, automatic vision system

C. Liu, W. T. Freeman, S. Bing Kang, R. Szeliski and C. L. Zitnick, "Automatic Estimation and Removal of Noise from a Single Image," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 30, no. , pp. 299-314, 2007.
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